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Customer support has always been considered a competitive advantage in many industries. In recent years, firms have begun to provide customers with a high-quality service experience, in order to attract more customers and achieve higher customer satisfaction. Although customer service and satisfaction have been discussed by other researchers, to the knowledge, there has been no dynamic and intelligent way to model and optimize customer support systems for product and service providers. The purpose of this paper is to develop a modeling method for customer support optimization.

In this study, a system dynamics (SD) model has been formulated to investigate the dynamic characteristics of customer support in an IT service provider. The proposed simulation model considers the dynamic, non-linear, and asymmetric interactions among its components, and allows study of the behavior of the customer support system under controlled conditions. Furthermore, a particle swarm optimization method was developed to investigate the proper combination of parameters and strategy development of the support center.

This paper proposes a novel modeling, simulation, and optimization approach for complex customer support systems of information and communications technology (ICT) service providers. This method helps managers improve their customer support systems. Moreover, the simulation results of the case study show that ICT service providers can gain benefit by managing their customer service dynamically over time using the proposed artificial intelligent multi-parameter modeling and optimization method.

The proposed holistic modeling approach and multi-parameter optimization method will greatly help managers and researchers understand the factors influencing customer support. Moreover, it facilitates the process of making new improvement strategies based on provided insights.

The paper shows how SD simulation and multi-parameter optimization can provide insights into the field of customer support. However, the existing literature lacks a holistic view of these kinds of simulation systems, as well as a multi-parameter optimization method for SD methodology.

The purpose of this study is to develop a method to reduce the computation time necessary for the automated optimal design of dual-band wearable antennas. In particular, the authors investigated if this can be achieved by the use of a hierarchical optimization paradigm combined with a simplified human body model. The geometry of the antenna under consideration is described via eight geometrical parameters which are automatically adjusted with the use of an evolutionary algorithm to improve the impedance matching of an antenna located in the proximity of a human body. Specifically, the antennas were designed to operate in the ISM band which covers two frequency ranges: 2.4-2.5 GHz and 5.7-5.9 GHz.

During the studies on the automated design of wearable antennas using evolutionary computing, the authors observed that not all design parameters exhibit equal influence on the objective function. Therefore, it was hypothesized that to reduce the computation effort, the design parameters can be activated sequentially based on their influence. Accordingly, the authors’ computer code has been modified to include this feature.

The authors’ novel hierarchical multi-parameter optimization method was able to converge to a better solution within a shorter time compared to an equivalent method not exploiting automatic activation of an increasing number of design parameters. Considering a significant computational cost involved in the calculation of the objective function, this exhibits a convincing advantage of their hierarchical approach, at least for the considered class of antennas.

The described method has been developed for the design of single- or dual-band wearable antennas. Its application to other classes of antennas and antenna environments may require some adjustments of the objective functions or parameter values of the evolutionary algorithm. It follows from the well-recognized fact that all optimization methods are to some extent application-specific.

Computation load involved in the automated design and optimization can be significantly reduced compared to the non-hierarchical approach with a heterogeneous human body model.

To the best of the authors’ knowledge, the described application of hierarchical paradigm to the optimization of wearable antennas is fully original, as well as is its combination with simplified body models.

Sequential application of fractional factorial and response surface designs in the regression modelling and optimisation of a multi‐parameter type manufacturing process is presented. In particular, the coating thickness variation of an acid copper plating process was minimised with high bath acidity, high cathodic current density and large anode‐cathode separation. Statistically designed experiments are shown to be highly effective in studying the effects and interactions of the various process factors.

This study aims to complete the optimization design of a centrifugal impeller with both high aerodynamic efficiency and good structural machinability.

First, the design parameters were derived from the blade loading distribution and the meridional geometry in the impeller three-dimensional (3D) inverse design. The blade wrap angle at the middle span surface and the spanwise averaged blade angle at the blade leading edge obtained from inverse design were chosen as the machinability objectives. The aerodynamic efficiency obtained by computational fluid dynamics was selected as the aerodynamic performance objective. Then, using multi-objective optimization with the optimal Latin hypercube method, quadratic response surface methodology and the non-dominated sorting genetic algorithm, the trade-off optimum impellers with small blade wrap angles, large blade angles and high aerodynamic efficiency were obtained. Finally, computational fluid dynamics and computer-aided manufacturing were performed to verify the aerodynamic performance and structural machinability of the optimum impellers.

Providing the fore maximum blade loading distribution at both the hub and shroud for the 3D inverse design helped to promote the structural machinability of the designed impeller. A straighter hub coupled with a more curved shroud also facilitated improvement of the impeller’s structural machinability. The preferred impeller was designed by providing both the fore maximum blade loading distribution at a relatively straight hub and a curved shroud for 3D inverse design.

The machining difficulties of the designed high-efficiency impeller can be reduced by reducing blade wrap angle and enlarging blade angle at the beginning of impeller design. It is of practical value in engineering by avoiding the follow-up failure for the machining of the designed impeller.

The purpose of this paper is to present an algorithm for global optimization of high‐dimensional real‐parameter cost functions.

This optimization algorithm, called differential ant‐stigmergy algorithm (DASA), based on a stigmergy observed in colonies of real ants. Stigmergy is a method of communication in decentralized systems in which the individual parts of the system communicate with one another by modifying their local environment.

The DASA outperformed the included differential evolution type algorithm in convergence on all test functions and also obtained better solutions on some test functions.

The DASA may find applications in challenging real‐life optimization problems such as maximizing the empirical area under the receiver operating characteristic curve of glycomics mass spectrometry data and minimizing the logistic leave‐one‐out calculation measure for the gene‐selection criterion.

The DASA is one of the first ant‐colony optimization‐based algorithms proposed for global optimization of the high‐dimensional real‐parameter problems.

The purpose of this paper is to apply a hybrid algorithm based on the combination of two heuristics inspired by artificial life to the solution of optimization problems.

The flock‐of‐starlings optimization (FSO) and the bacterial chemotaxis algorithm (BCA) were adapted to implement a hybrid and parallel algorithm: the FSO has been powerfully employed for exploring the whole space of solutions, whereas the BCA has been used to refine the FSO‐found solutions, thanks to its better performances in local search.

A good solution of the 8‐th parameters version of the TEAM problem 22 is obtained by using a maximum 200 FSO steps combined with 20 BCA steps. Tests on an analytical function are presented in order to compare FSO, PSO and FSO+BCA algorithms.

The development of an efficient method for the solution of optimization problems, exploiting the different characteristic of the two heuristic approaches.

The paper shows the combination and the interaction of stochastic methods having different exploration properties, which allows new algorithms able to produce effective solutions of multimodal optimization problems, with an acceptable computational cost, to be defined.

This study aims to propose an efficient method for solving reliability-based design optimization (RBDO) problems.

In the proposed algorithm, genetic algorithm (GA) is employed to search the global optimal solution of design parameters satisfying the reliability and deterministic constraints. The Kriging model based on U learning function is used as a classification tool to accurately and efficiently judge whether an individual solution in GA belongs to feasible region.

Compared with existing methods, the proposed method has two major advantages. The first one is that the GA is employed to construct the optimization framework, which is helpful to search the global optimum solutions of the RBDO problems. The other one is that the use of Kriging model is helpful to improve the computational efficiency in solving the RBDO problems.

Since the boundaries are concerned in two Kriging models, the size of the training set for constructing the convergent Kriging model is small, and the corresponding efficiency is high.

This study aims to present a method for the conceptual design and simulation of an aircraft flight control system.

The design methodology is based on particle swarm optimization (PSO). PSO can be used to improve the performance of conventional controllers. The aim of the present study is threefold. First, it attempts to detect and isolate faults in an aircraft model. Second, it is to design a proportional (P) controller, a proportional derivative (PD) controller, a proportional-integral (PI) controller and a fuzzy controller for an aircraft model. Third, it is to design a PD controller for an aircraft using a PSO algorithm.

Conventional controllers, an intelligent controller and a PD controller-based PSO were investigated for flight control. It was seen that the P controller, the PI controller and the PD controller-based PSO caused overshoot. These overshoots were 18.5, 87.7 and 2.6 per cent, respectively. Overshoot was not seen using the PD controller or fuzzy controller. Steady state errors were almost zero for all controllers. The PD controller had the best settling time. The fuzzy controller was second best. The PD controller-based PSO was the third best, but the result was close to the others.

This study shows the implementation of the present algorithm for a specified space mission and also for study regarding variation of performance parameters. This study shows fault detection and isolation procedures and also controller gain choice for a flight control system. A comparison between conventional controllers and PD-based PSO controllers is presented. In this study, sensor fault detection and isolation are carried out, and, also, root locus, time domain analysis and Routh–Hurwitz methods are used to find the conventional controller gains which differ from other studies. A fuzzy controller is created by the trial and error method. Integral of squared time multiplied by squared error is used as a performance function type in PSO.

The purpose of this paper is to provide a case study on endorsing process improvement in maritime operations by implementing design of experiments on Lean Six Sigma performance responses. It is demonstrated how process efficiency and environmental muda may be dealt with simultaneously in a lean‐and‐green project driven by hardcore Six Sigma tools.

A 16‐run Taguchi‐type orthogonal design was employed to gather data for vessel speed (VS), exhaust gas temperature (EGT) and fuel consumption (FC) as modulated by a total of 15 controlling parameters synchronously. Active dependencies were inferred based on the desirability analysis method on direct process data from a performance log. This log was maintained for a long‐term monitoring during sea voyages of a double skin bulk carrier of 55,000 DWT while in sea service.

A high composite desirability value was achieved eclipsing the 0.90 mark. Values well over the 0.9 level were also obtained for the three examined individual desirability values of VS, EGT and FC. Leading controlling parameters were discovered to be compressor pressure, fuel pump index, slip, governor index and MIP.

A Lean Six Sigma project is carried out to improve performance characteristics in ordinary maritime operations. While the company in the case study outlined in this article no longer relies on periodic inspections to determine machinery conditions, improvement on key process characteristics were nevertheless deemed worthy of ameliorating. Information retrieval from computerized continuous monitoring systems assisted in conducting experimental designs in order to obtain optimal performance. Specifically, the tuning of vessel main engine running mode was examined aiming at increasing the quality levels of output power to the shaft along with a reduction of NOx emissions.

This work adds an interesting paradigm in the critical field of maritime activities for processes in full gear while operating at sea. Maritime operations are an imperative necessity when expediting international trading transactions. It is the first time that such a case study has emanated from a real pilot Lean Six Sigma project which interlaces process efficiency enhancement with concurrent environmental muda reduction.

This paper aims to review seven artificial intelligence tools that are useful in assembly automation: knowledge‐based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case‐based reasoning and ambient‐intelligence.

Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation.

Artificial intelligence has produced a number of useful and powerful tools. This paper reviews some of those tools. Applications of these tools in assembly automation have become more widespread due to the power and affordability of present‐day computers.

Many new assembly automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single robots or systems with low‐capability microcontrollers.

It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with using these tools and it may take a long time for direct digital manufacturing to be considered commonplace… but it is expanding. The appropriate deployment of the new AI tools will contribute to the creation of more competitive assembly automation systems.

Other technological developments in AI that will impact on assembly automation include data mining, multi‐agent systems and distributed self‐organising systems.

The novel approaches proposed use ambient intelligence and the mixing of different AI tools in an effort to use the best of each technology. The concepts are generically applicable across all industrial assembly processes and this research is intended to prove that the concepts work in manufacturing.